Evaluation of fibroblast activation protein-specific PET/CT in a patient with post-COVID pneumonitis.

Coronavirus disease 2019 (COVID-19) often leads to a spectrum of pulmonary complications, including interstitial lung disease (ILD) with the potential for fibrotic sequelae. Assessing the presence of ongoing active inflammation versus established residual fibrosis as a result of lung parenchymal injury and repair in these patients is a clinical challenge. Better understanding of the disease process is crucial for guiding appropriate therapeutic strategies. We aim to investigate the use of positron emission tomography / computer tomography (PET/CT) scans and their role in diagnosing interstitial pneumonitis (IP) post COVID infections.

Anatomical pattern of entheseal and synovial fibroblast activation in patients with psoriasis and its risk of developing psoriatic arthritis.

OBJECTIVES: To assess the presence and anatomical distribution of activated fibroblasts in the joints and entheses of patients with psoriasis with arthralgia and to test how fibroblast activation visualised by 68gallium-labelled fibroblast activation protein inhibitor-04 (68Ga-FAPI-04)-positron emission tomography (PET)/CT correlates with clinical tenderness, musculoskeletal ultrasound findings and progression to psoriatic arthritis (PsA). METHODS: We conducted a prospective cohort study in patients with psoriasis and arthralgia who underwent clinical and ultrasound evaluation and whole-body PET/CT imaging with 68Ga-FAPI-04. 68Ga-FAPI-04 uptake at synovial and entheseal sites was assessed by maximal standardised uptake values (SUVmax) and PET/CT Joint Index (JI); logistic regression models were used to investigate its correlation with clinical and ultrasound findings. Survival analyses were performed on patients with at least 6 months of follow-up. RESULTS: 36 patients with psoriasis were enrolled. 68Ga-FAPI-04 uptake was found in 318 (7.9%) joints and 369 (7.3%) entheses in 29 (80.6%) participants, with a mean SUVmax (SD) of 3.2 (1.8) for joints and 2.9 (1.6) for entheses. Large joints and the lower limbs were predominantly affected. A significant positive relationship was found between 68Ga-FAPI-04-PET/CT signal intensity and the 68 tender joint count (SUVmax: p<0.001; PET/CT-JI: p<0.001) and tender entheses count (SUVmax: p<0.001; PET/CT-JI: p=0.002). No correlations were found with ultrasound findings (SUVmax: p=0.969; PET/CT-JI: p=0.720). Patients with relevant synovio-entheseal 68Ga-FAPI-04 uptake showed a statistically significant higher risk of developing PsA (p=0.02), independent of ultrasound findings. CONCLUSIONS: Patients with psoriasis presenting with arthralgia show localised signs of resident tissue activation in joints and entheses, which are associated with higher risk of developing PsA.

Metabolic and molecular imaging in inflammatory arthritis.

It is known that metabolic shifts and tissue remodelling precede the development of visible inflammation and structural organ damage in inflammatory rheumatic diseases such as the inflammatory arthritides. As such, visualising and measuring metabolic tissue activity could be useful to identify biomarkers of disease activity already in a very early phase. Recent advances in imaging have led to the development of so-called 'metabolic imaging' tools that can detect these changes in metabolism in an increasingly accurate manner and non-invasively.Nuclear imaging techniques such as 18F-D-glucose and fibroblast activation protein inhibitor-labelled positron emission tomography are increasingly used and have yielded impressing results in the visualisation (including whole-body staging) of inflammatory changes in both early and established arthritis. Furthermore, optical imaging-based bedside techniques such as multispectral optoacoustic tomography and fluorescence optical imaging are advancing our understanding of arthritis by identifying intra-articular metabolic changes that correlate with the onset of inflammation with high precision and without the need of ionising radiation.Metabolic imaging holds great potential for improving the management of patients with inflammatory arthritis by contributing to early disease interception and improving diagnostic accuracy, thereby paving the way for a more personalised approach to therapy strategies including preventive strategies. In this narrative review, we discuss state-of-the-art metabolic imaging methods used in the assessment of arthritis and inflammation, and we advocate for more extensive research endeavours to elucidate their full field of application in rheumatology.

CD200+ fibroblasts form a pro-resolving mesenchymal network in arthritis.

Fibroblasts are important regulators of inflammation, but whether fibroblasts change phenotype during resolution of inflammation is not clear. Here we use positron emission tomography to detect fibroblast activation protein (FAP) as a means to visualize fibroblast activation in vivo during inflammation in humans. While tracer accumulation is high in active arthritis, it decreases after tumor necrosis factor and interleukin-17A inhibition. Biopsy-based single-cell RNA-sequencing analyses in experimental arthritis show that FAP signal reduction reflects a phenotypic switch from pro-inflammatory MMP3+/IL6+ fibroblasts (high FAP internalization) to pro-resolving CD200+DKK3+ fibroblasts (low FAP internalization). Spatial transcriptomics of human joints indicates that pro-resolving niches of CD200+DKK3+ fibroblasts cluster with type 2 innate lymphoid cells, whereas MMP3+/IL6+ fibroblasts colocalize with inflammatory immune cells. CD200+DKK3+ fibroblasts stabilized the type 2 innate lymphoid cell phenotype and induced resolution of arthritis via CD200-CD200R1 signaling. Taken together, these data suggest a dynamic molecular regulation of the mesenchymal compartment during resolution of inflammation.

Imaging in inflammatory arthritis: progress towards precision medicine.

Imaging techniques such as ultrasonography and MRI have gained ground in the diagnosis and management of inflammatory arthritis, as these imaging modalities allow a sensitive assessment of musculoskeletal inflammation and damage. However, these techniques cannot discriminate between disease subsets and are currently unable to deliver an accurate prediction of disease progression and therapeutic response in individual patients. This major shortcoming of today's technology hinders a targeted and personalized patient management approach. Technological advances in the areas of high-resolution imaging (for example, high-resolution peripheral quantitative computed tomography and ultra-high field MRI), functional and molecular-based imaging (such as chemical exchange saturation transfer MRI, positron emission tomography, fluorescence optical imaging, optoacoustic imaging and contrast-enhanced ultrasonography) and artificial intelligence-based data analysis could help to tackle these challenges. These new imaging approaches offer detailed anatomical delineation and an in vivo and non-invasive evaluation of the immunometabolic status of inflammatory reactions, thereby facilitating an in-depth characterization of inflammation. By means of these developments, the aim of earlier diagnosis, enhanced monitoring and, ultimately, a personalized treatment strategy looms closer.

Ability of 18F-FDG Positron Emission Tomography Radiomics and Machine Learning in Predicting KRAS Mutation Status in Therapy-Naive Lung Adenocarcinoma.

OBJECTIVE: Considering the essential role of KRAS mutation in NSCLC and the limited experience of PET radiomic features in KRAS mutation, a prediction model was built in our current analysis. Our model aims to evaluate the status of KRAS mutants in lung adenocarcinoma by combining PET radiomics and machine learning. METHOD: Patients were retrospectively selected from our database and screened from the NSCLC radiogenomic dataset from TCIA. The dataset was randomly divided into three subgroups. Two open-source software programs, 3D Slicer and Python, were used to segment lung tumours and extract radiomic features from 18F-FDG-PET images. Feature selection was performed by the Mann-Whitney U test, Spearman's rank correlation coefficient, and RFE. Logistic regression was used to build the prediction models. AUCs from ROCs were used to compare the predictive abilities of the models. Calibration plots were obtained to examine the agreements of observed and predictive values in the validation and testing groups. DCA curves were performed to check the clinical impact of the best model. Finally, a nomogram was obtained to present the selected model. RESULTS: One hundred and nineteen patients with lung adenocarcinoma were included in our study. The whole group was divided into three datasets: a training set (n = 96), a validation set (n = 11), and a testing set (n = 12). In total, 1781 radiomic features were extracted from PET images. One hundred sixty-three predictive models were established according to each original feature group and their combinations. After model comparison and selection, one model, including wHLH_fo_IR, wHLH_glrlm_SRHGLE, wHLH_glszm_SAHGLE, and smoking habits, was validated with the highest predictive value. The model obtained AUCs of 0.731 (95% CI: 0.619~0.843), 0.750 (95% CI: 0.248~1.000), and 0.750 (95% CI: 0.448~1.000) in the training set, the validation set and the testing set, respectively. Results from calibration plots in validation and testing groups indicated that there was no departure between observed and predictive values in the two datasets (p = 0.377 and 0.861, respectively). CONCLUSIONS: Our model combining 18F-FDG-PET radiomics and machine learning indicated a good predictive ability of KRAS status in lung adenocarcinoma. It may be a helpful non-invasive method to screen the KRAS mutation status of heterogenous lung adenocarcinoma before selected biopsy sampling.